PID controllers in HVAC waste 30% more energy than necessary due to lack of adaptation to weather and occupancy. In a 10,000 m² office building, this results in 1.5–2 million rubles in annual overspending. Implementation cost for a similar building starts from 4 million rubles, yielding ROI in under 2 years. A digital twin solves the problem: it synchronizes with the physical object via sensors and optimizes engineering systems in real time. We have implemented over 50 projects—from office centers to manufacturing facilities. Certified engineers guarantee quality implementation and KPI achievement. Our solutions leverage AI for buildings, BIM intelligent building models, and ML for buildings to deliver accurate predictions.
Why a building digital twin is more efficient than traditional BMS
Traditional BMS operate on rigid schedules—night setback, fixed timetables. They don't adapt to actual occupancy, weather changes, or tariffs. A digital twin uses ML for forecasting: MPC (Model Predictive Control) reduces HVAC energy consumption by 15–25%, which is 3–5 times more efficient than PID controllers. Additionally, fault detection is up to 10 times faster compared to manual inspections. The system accounts for a 48-hour weather forecast, occupancy based on CO₂ and PIR, time-of-use tariffs—and automatically selects optimal setpoints.
Digital twin architecture
The model is built on four layers:
- Geometric: BIM model (Revit, IFC)—geometry, structures, engineering networks.
- Semantic: building ontology—rooms, zones, equipment, relationships.
- Real-time data: sensors, meters, SCADA.
- Predictive: ML models—future state, risks, recommendations.
Technology stack:
BIM (Revit/OpenBIM) → IFC conversion ↓ Knowledge Graph (Apache Jena, Stardog) — building ontology graph ↓ IoT Data Platform (ThingsBoard, AWS IoT Core) — sensor data ↓ Digital Twin Platform (Bentley iTwin, Azure Digital Twins, Siemens Xcelerator) ↓ ML/Analytics Engine (Python, PyTorch, scikit-learn) ↓ Dashboard (Grafana, custom React/3D WebGL) The BIM model is converted into a Knowledge Graph, which links each sensor to a specific room and equipment. The IoT platform aggregates data via BACnet, Modbus, KNX. ML modules build forecasts, and dashboards visualize the state in real time. We provide energy consumption optimization through AI-driven control. Our IoT for buildings platform aggregates sensor data.
Thermal model and HVAC optimization
RC Thermal Network (physical model):
class ThermalZoneModel: def __init__(self, C_zone, R_wall, R_window, R_hvac): self.C = C_zone self.R_wall = R_wall self.R_window = R_window self.R_hvac = R_hvac def next_temperature(self, T_zone, T_outdoor, T_supply_air, Q_occupants, dt): Q_wall = (T_outdoor - T_zone) / self.R_wall Q_window = (T_outdoor - T_zone) / self.R_window Q_hvac = (T_supply_air - T_zone) / self.R_hvac Q_total = Q_wall + Q_window + Q_hvac + Q_occupants dT = Q_total / self.C * dt return T_zone + dT MPC optimizes setpoints over a 24–48 hour horizon, minimizing energy cost subject to comfort constraints. The RC thermal building model is calibrated using historical data and real-time sensors. See full code in the original.
How we calibrate the building thermal model
RC parameters (C, R) are estimated from historical data via Bayesian optimization or scipy.optimize. For real-time, we use Kalman Filter. This allows the model to accurately predict thermal dynamics even with changing weather and occupancy.
Comparison of HVAC control methods
| Parameter | PID controller | MPC (Model Predictive Control) |
|---|---|---|
| Energy consumption | Baseline | -15-25% |
| Weather adaptation | No | Accounts for 48h forecast |
| Tariff optimization | No | Optimizes based on time-of-use |
| Comfort | Fixed setpoint | Dynamic within defined limits |
| Implementation complexity | Low | Medium (model required) |
Digital twin vs. traditional BMS
| Feature | Digital Twin (AI) | Traditional BMS |
|---|---|---|
| Adaptation to changes | Automatic, ML | Manual scenarios |
| Forecasting | Yes (24-48 h) | No |
| Energy efficiency | 15-30% | Baseline |
| Predictive maintenance | Yes | Reactive only |
| IoT integration | Built-in | Via gateways |
How we integrate digital twin with your BMS
We do not replace existing equipment. The digital twin overlays the BMS/BAS via BACnet, Modbus, or KNX. Data from controllers flows into the IoT platform, where it is enriched with weather and occupancy data. ML models build forecasts, and optimized setpoints are sent back to the BMS. The entire cycle takes 5 to 15 seconds—fast enough for real-time operation. Get a preliminary efficiency assessment for your building in 1 day.
Project implementation stages
- Building audit—collecting BIM, sensors, BMS data, analyzing current consumption.
- Thermal model development—calibrating RC parameters, training ML models.
- Platform deployment—Azure Digital Twins or Bentley iTwin, Knowledge Graph setup.
- Integration with CMMS, ERP, tenant billing—transferring data to adjacent systems.
- Staff training—working with dashboards, interpreting forecasts.
- 12-month warranty—support and refinements as needed.
What's included
- Building audit report with efficiency assessment
- Calibrated thermal model (RC parameters)
- Digital twin platform (Azure Digital Twins or Bentley iTwin)
- Integration with existing BMS/BAS
- Custom dashboard (Grafana or 3D WebGL)
- Staff training (2 sessions)
- 12 months of technical support and updates
Example project: 15,000 m² office center
Energy savings—2.3 million rubles/year (28% of baseline consumption). Equipment failures reduced by 70%. Temperature prediction accuracy—RMSE 0.4°C.What KPIs do we guarantee?
Target metrics are fixed in the contract: energy savings (15–30%), reduction in emergency failures (up to 70%), temperature forecast accuracy (RMSE < 0.5°C). If KPIs are not met, rework at our expense. For a small facility (5,000 m²), average savings are about 0.8 million rubles/year.
Our experience
Our team consists of engineers with experience in AI/ML, certified in Microsoft Azure and Bentley iTwin. Over 50 facilities in Russia and CIS already use our solutions. Request a consultation on implementing a digital twin for your building. We will assess the project for free and prepare a commercial proposal.
Based on Wikipedia: Digital Twin